如何摆脱"UserWarning: The initializer GlorotUniform is unseeded"消息？

Question

我有以下代码：模型评估的偏方差分解

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from mlxtend.evaluate import bias_variance_decomp
from mlxtend.data import boston_housing_data
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
np.random.seed(16)
tf.random.set_seed(16)
X, y = boston_housing_data()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.3,
random_state=123,
shuffle=True)
model = Sequential()
model.add(Dense(2048, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='linear'))
optimizer = tf.keras.optimizers.Adam()
model.compile(loss='mean_squared_error', optimizer=optimizer)
model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=0)
mean_squared_error(model.predict(X_test), y_test)
avg_expected_loss, avg_bias, avg_var = bias_variance_decomp(
model, X_train, y_train, X_test, y_test, 
loss='mse',
num_rounds=100,
random_seed=16,
epochs=100,
batch_size=32,
verbose=0)
print('Average expected loss: %.3f' % avg_expected_loss)
print('Average bias: %.3f' % avg_bias)
print('Average variance: %.3f' % avg_var)

《守则》行之有效。然而，它会产生一个令人讨厌的警告：

UserWarning：初始化程序GlorotUniform是非种子的，并且被调用多次，每次都会返回相同的值(即使初始化程序是非种子(。请更新您的代码以向初始化器提供种子，或者避免多次使用同一个初始化器实例。warnings.warn(

为了消除警告，需要对代码进行哪些更改？

Answer 1

正如警告消息所说，需要向初始值设定项提供种子。只需将代码更改为：

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from mlxtend.evaluate import bias_variance_decomp
from mlxtend.data import boston_housing_data
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from keras import initializers
np.random.seed(16)
tf.random.set_seed(16)
X, y = boston_housing_data()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.3,
random_state=123,
shuffle=True)
model = Sequential()
model.add(Dense(2048, activation='relu', 
kernel_initializer=initializers.glorot_uniform(seed=0)))
model.add(Dense(512, activation='relu', 
kernel_initializer=initializers.glorot_uniform(seed=0)))
model.add(Dense(32, activation='relu', 
kernel_initializer=initializers.glorot_uniform(seed=0)))
model.add(Dense(1, activation='linear', 
kernel_initializer=initializers.glorot_uniform(seed=0)))
optimizer = tf.keras.optimizers.Adam()
model.compile(loss='mean_squared_error', optimizer=optimizer)
model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=0)
mean_squared_error(model.predict(X_test), y_test)
avg_expected_loss, avg_bias, avg_var = bias_variance_decomp(
model, X_train, y_train, X_test, y_test, 
loss='mse',
num_rounds=10,
random_seed=16,
epochs=10,
batch_size=32,
verbose=0)
print('Average expected loss: %.3f' % avg_expected_loss)
print('Average bias: %.3f' % avg_bias)
print('Average variance: %.3f' % avg_var)